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language:
- ar
- as
- bn
- ca
- en
- es
- eu
- fr
- gu
- hi
- id
- ig
- mr
- pa
- pt
- sw
- ur
- vi
- yo
- zh
- multilingual
datasets:
- wikiann
xlm-roberta-base-wikiann-ner
Model description
xlm-roberta-base-wikiann-ner is the first Named Entity Recognition model for 20 languages (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese) based on a fine-tuned XLM-RoBERTa large model. It achieves the state-of-the-art performance for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a xlm-roberta-large model that was fine-tuned on an aggregation of languages datasets obtained from WikiANN dataset.
Intended uses & limitations
How to use
You can use this model with Transformers pipeline for NER.
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base-wikiann-ner")
model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-base-wikiann-ner")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Ìbọn ń ró kù kù gẹ́gẹ́ bí ọwọ́ ọ̀pọ̀ aráàlù ṣe tẹ ìbọn ní Kyiv láti dojú kọ Russia"
ner_results = nlp(example)
print(ner_results)
Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
This model was fine-tuned on 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi, Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)wikiann.
The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
| Abbreviation | Description |
|---|---|
| O | Outside of a named entity |
| B-PER | Beginning of a person’s name right after another person’s name |
| I-PER | Person’s name |
| B-ORG | Beginning of an organisation right after another organisation |
| I-ORG | Organisation |
| B-LOC | Beginning of a location right after another location |
| I-LOC | Location |
BibTeX entry and citation info